Beliefs that lunar phases affect human physiology started in ancient times. Research has recently revealed that a physical fitness index increased in sedentary students at the new moon (NM) and full moon (FM) compared to other moon phases. However, the effect of lunar cycle (moon illumination and gravitational pull) on physical performance in athletes was not examined. Therefore, this study aimed to evaluate whether short-term explosive performance can be influenced by the different phases of the lunar cycle. Fourteen young male Taekwondo athletes (age: 16.9 ± 0.7 years, height: 159.7 ± 50.6 cm, body mass: 62.85 ± 7.84 kg) performed the following tests to assess the explosive physical performance during the different phases of the lunar cycle (NM, FQ (first quarter), FM, and LQ (last quarter)): maximal isometric manual contraction (dominant hand (MIMCD) and non-dominant hand (MIMCND)), maximal back isometric contraction (MBIC), squat jump (SJ), countermovement jump (CMJ), and 10-m sprint (10 m). The testing sessions during the different moon phases were performed in a counterbalanced order. The order of tests remained the same (MIMCD, MIMCND, MBIC, SJ, CMJ, and 10 m), and all sessions were performed in the evening (6:00 to 8:00 p.m.) on the first day of each evaluated lunar phase. Each parameter was measured over two consecutive lunar months in the calendar. Analysis of variance tests showed that there was no significant effect of lunar cycle on all explosive test measures, p > 0.05. Our results failed to identify any effect of lunar phase on evening explosive performance (mainly involving phosphagen pathway-based efforts) among young trained athletes. Therefore, it appears that moon phase/illumination does not affect short-term physical performance in young trained adolescents.
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http://dx.doi.org/10.1080/07420528.2017.1422741 | DOI Listing |
Sci Rep
January 2025
College of Physical Education and Health Sciences, Zhejiang Normal University, Jinhua, 321004, China.
Athlete engagement is influenced by several factors, including cohesion, passion and mental toughness. Machine learning methods are frequently employed to construct predictive models as a result of their high efficiency. In order to comprehend the effects of cohesion, passion and mental toughness on athlete engagement, this study utilizes the relevant methods of machine learning to construct a prediction model, so as to find the intrinsic connection between them.
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January 2025
Department of Psychology, Faculty of Behavioural and Social Sciences, University of Groningen, Grote Kruisstraat 2/1, 9712TS, Groningen, The Netherlands.
Recruits are exposed to high levels of psychological and physical stress during the special forces selection period, resulting in dropout rates of up to 80%. To identify who likely drops out, we assessed a group of 249 recruits, every week of the selection program, on their self-efficacy, motivation, experienced psychological and physical stress, and recovery. Using linear regression as well as state-of-the-art machine learning techniques, we aimed to build a model that could meaningfully predict dropout while remaining interpretable.
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January 2025
Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, CT, 06269, USA.
Wearable and implantable bioelectronics that can interface for extended periods with highly mobile organs and tissues across a broad pH range would be useful for various applications in basic biomedical research and clinical medicine. The encapsulation of these systems, however, presents a major challenge, as such devices require superior barrier performance against water and ion penetration in challenging pH environments while also maintaining flexibility and stretchability to match the physical properties of the surrounding tissue. Current encapsulation materials are often limited to near-neutral pH conditions, restricting their application range.
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January 2025
School of Physical Science and Technology, Ningbo University, Ningbo, 315211, China.
The high performance of two-dimensional (2D) channel membranes is generally achieved by preparing ultrathin or forming short channels with less tortuous transport through self-assembly of small flakes, demonstrating potential for highly efficient water desalination and purification, gas and ion separation, and organic solvent waste treatment. Here, we report the construction of vertical channels in graphene oxide (GO) membrane based on a substrate template with asymmetric pores. The membranes achieved water permeance of 2647 L m h bar while still maintaining an ultrahigh rejection rate of 99.
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January 2025
KAUST Solar Center (KSC), Physical and Engineering Division (PSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.
The controlled growth of two-dimensional (2D) perovskite atop three-dimensional (3D) perovskite films reduces interfacial recombination and impedes ion migration, thus improving the performance and stability of perovskite solar cells (PSCs). Unfortunately, the random orientation of the spontaneously formed 2D phase atop the pre-deposited 3D perovskite film can deteriorate charge extraction owing to energetic disorder, limiting the maximum attainable efficiency and long-term stability of the PSCs. Here, we introduce a meta-amidinopyridine ligand and the solvent post-dripping step to generate a highly ordered 2D perovskite phase on the surface of a 3D perovskite film.
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